Abstract
Autonomous underwater manipulation is nowadays still an open research challenge. This paper describes the approaches to tackle some of the open challenges. On the one side, the use of machine learning techniques for the online identification and adaption of vehicle dynamics (dealing with drift compensation, mass changes, etc.) as well as the use of high-level context-based configuration of controllers to adapt to changes in system morphology, hardware, and/or tasks. On the other side, a robust control of underwater manipulators based on an extension of whole-body control techniques is envisaged which takes into account the heterogeneous actuation (thrusters on the base, actuators on the arm joints) as well as the uncertain underwater vehicle dynamics. The result is a highly-reconfigurable system that can automatically adapt its behavior to cope with changes in the environment, in its own morphology and/or in the task goals. The outcomes are planned to be validated in two different scenarios: a floating-base dynamics testbed originating from space applications and aerial robots at DLR and an underwater pool at DFKI.
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Antonelli G (2006) Underwater robots: motion and force control of vehicle-manipulator systems. Springer tracts in advanced robotics, vol 2
Bargsten V, de Gea Fernández J, Kassahun Y (2016) Experimental robot inverse dynamics identification using classical and machine learning techniques. In: Proceedings of ISR 2016: 47st international symposium on robotics, pp 1–6
Daniel C, Kroemer O, Viering M, Metz J, Peters J (2015) Active reward learning with a novel acquisition function. Auton Robots 39(3):389–405
de Gea Fernández J, Mronga D, Günther M, Knobloch T, Wirkus M, Schröer M, Trampler M, Stiene S, Kirchner E, Bargsten V, Bänziger T, Teiwes J, Krüger T, Kirchner F (2017) Multimodal sensor–based whole–body control for human–robot collaboration in industrial settings. Robot Auton Syst 94(Supplement C):102 – 119. https://doi.org/10.1016/j.robot.2017.04.007
Dietrich A, Bussmann K, Petit F, Kotyczka P, Ott C, Lohmann B, Albu-Schäffer A (2015) Whole-body impedance control of wheeled mobile manipulators: stability analysis and experiments on the humanoid robot rollin’ justin. Auton Robots
Fossen TI (2002) Marine control systems: guidance, navigation and control of ships, rigs and underwater vehicles
Garofalo G, Henze B, Englsberger J, Ott C (2015) On the inertially decoupled structure of the floating base robot dynamics. In: Proceedings of 8th vienna international conference on mathematical modelling (MATHMOD), pp 322–327
Giordano AM, Garofalo G, Stefano MD, Ott C, Albu-Schaeffer A (2016) Dynamics and control of a free-floating space robot in presence of nonzero linear and angular momenta. In: Proceedings of IEEE annual conference on decision and control (CDC), pp 322–327
Hanff H, Kloss P, Wehbe B, Kampmann P, Kroffke S, Sander A, Firvida MB, von Einem M, Bode JF, Kirchner F (2017) Auvx—a novel miniaturized autonomous underwater vehicle. In: OCEANS 2017—Aberdeen, pp 1–10. https://doi.org/10.1109/OCEANSE.2017.8084946
Henze B, Dietrich A, Ott C (2016) An approach to combine balancing and multi-objective manipulation for legged humanoid robots. IEEE Robot Autom Lett 1(2):700–707
Kim M, Kondak K, Ott C (2018) A stabilizing controller for regulation of uav with manipulator. IEEE Robot Autom Lett (2018)
Martinez-Cantin R, Lopes M, Montesano L (2010) Body schema acquisition through active learning. In: 2010 IEEE international conference on robotics and automation, pp 1860–1866. https://doi.org/10.1109/ROBOT.2010.5509406
Nguyen-Tuong D, Peters J (2010) Using model knowledge for learning inverse dynamics. In: 2010 IEEE international conference on robotics and automation, pp 2677–2682 (2010). https://doi.org/10.1109/ROBOT.2010.5509858
Nguyen-Tuong D, Peters J (2011) Model learning for robot control: a survey. Cognit Process 12(4):319–340
Ott C, Dietrich A, Albu-Schäffer A (2015) Prioritized multi-task compliance control of redundant manipulators. Automatica 53:416–423
Schempf H, Yoerger DR (1992) Coordinated vehicle/manipulation design and control issues for underwater telemanipulation. IFAC Proc 25(3):259–267 (1992). IFAC workshop on artificial intelligence control and advanced technology in marine automation (CAMS ’92), Genova, Italy, April 8–10
Sentis L (2007) Synthesis and control of whole-body behaviors in humanoid systems. Ph.D. thesis, Stanford, CA, USA
Simetti E, Casalino G, Torelli S, Sperindé A, Turetta A, Floating underwater manipulation: developed control methodology and experimental validation within the trident project. J Field Robot 31(3):364–385. https://doi.org/10.1002/rob.21497
Swevers J, Verdonck W, Schutter JD (2007) Dynamic model identification for industrial robots. IEEE Control Syst Mag 27(5):58–71. https://doi.org/10.1109/MCS.2007.904659
Wehbe B, Fabisch A, Krell MM (2017) Online model identification for underwater vehicles through incremental support vector regression. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4173–4180. https://doi.org/10.1109/IROS.2017.8206278
Wehbe B, Hidebrandt M, Kirchner F (2017) Experimental evaluation of various machine learning regression methods for model identification of autonomous underwater vehicles. In: Proceedings of 2017 international conference on robotics and automation (ICRA). IEEE international conference on robotics and automation (ICRA-17), pp 4885–4890. IEEE Robotics and Automation Society, IEEE
Wehbe B, Krell MM (2017) Learning coupled dynamic models of underwater vehicles using support vector regression. In: OCEANS 2017—Aberdeen, pp 1–7. https://doi.org/10.1109/OCEANSE.2017.8084596
Yoerger DR, Schempf H, Dipietro DM, Design and performance evaluation of an actively compliant underwater manipulator for full-ocean depth. J Robot Syst 8(3):371–392. https://doi.org/10.1002/rob.4620080306
Yu B, de Gea Fernández J, Kassahun Y, Bargsten V (2017) Learning the elasticity of a series-elastic actuator for accurate torque control. In: Benferhat S, Tabia K, Ali M (eds) advances in artificial intelligence: from theory to practice: 30th international conference on industrial engineering and other applications of applied intelligent systems, IEA/AIE 2017, Arras, France, June 27–30, 2017, Proceedings, Part I, pp. 543–552. Springer International Publishing
Yuh J (2000) Design and control of autonomous underwater robots: a survey. Auton Robot 8(1):7–24. https://doi.org/10.1023/A:1008984701078
Yuh J, Choi SK, Ikehara C, Kim GH, McMurty G, Ghasemi-Nejhad M, Sarkar N, Sugihara K (1998) Design of a semi-autonomous underwater vehicle for intervention missions (sauvim). In: Proceedings of 1998 international symposium on underwater technology, pp 63–68. https://doi.org/10.1109/UT.1998.670059
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de Gea Fernández, J., Ott, C., Wehbe, B. (2020). Machine Learning and Dynamic Whole Body Control for Underwater Manipulation. In: Kirchner, F., Straube, S., Kühn, D., Hoyer, N. (eds) AI Technology for Underwater Robots. Intelligent Systems, Control and Automation: Science and Engineering, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-30683-0_9
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DOI: https://doi.org/10.1007/978-3-030-30683-0_9
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